216 lines
8.2 KiB
Python
216 lines
8.2 KiB
Python
# -*- coding: utf-8 -*-
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import csv
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import numpy as np
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from keras.layers import Dense, Activation, Embedding, Dropout, Conv1D, GlobalMaxPooling1D, Lambda
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from keras.layers import Input
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from keras.models import Model
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from keras.models import Sequential
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from tqdm import tqdm
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def getCiscoFeatures(curDataLine, urlSIPDict):
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numCiscoFeatures = 30
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try:
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ciscoFeatures = urlSIPDict[str(curDataLine['domain']) + str(curDataLine['server_ip'])]
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# print('cisco features: ' + str(ciscoFeatures))
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# log transform
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ciscoFeatures = np.log1p(ciscoFeatures, dtype='float32')
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# print('log transformed: ' + str(ciscoFeatures))
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return ciscoFeatures.ravel()
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except:
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return np.zeros([numCiscoFeatures, ]).ravel()
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def getCNNWithoutLastLayer(vocabSize, embeddingSize, input_length, filters, kernel_size,
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hidden_dims, drop_out):
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model = Sequential()
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model.add(Embedding(input_dim=vocabSize, output_dim=embeddingSize,
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input_length=input_length))
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model.add(Conv1D(filters,
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kernel_size,
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activation='relu'))
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# we use max pooling:
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model.add(GlobalMaxPooling1D())
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# We add a vanilla hidden layer:
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model.add(Dense(hidden_dims))
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model.add(Dropout(drop_out))
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model.add(Activation('relu'))
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return model
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def getCNNWitoutLastLayerFunctional(vocabSize, embeddingSize, input_length, filters, kernel_size,
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hidden_dims, drop_out):
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a = Input(shape=(input_length,))
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embedding = Embedding(input_dim=vocabSize, output_dim=embeddingSize)(a)
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conv1 = Conv1D(filters, kernel_size, activation='relu')(embedding)
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glob = GlobalMaxPooling1D()(conv1)
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dense = Dense(hidden_dims)(glob)
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drop = Dropout(drop_out)(dense)
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model = Activation('relu')(drop)
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model = Model(a, model)
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return model
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def getFlowFeatureLayer(numFeatures):
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model = Sequential()
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# slpModel.add(Dense(1, input_shape=(1,)))
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model.add(Lambda(lambda x: x + 0.0, input_shape=(numFeatures,)))
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return model
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def createCNNDataSet(domains, label, characterDict, maxLen=40):
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# process domains in reverse order
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outFeature = np.zeros([len(domains), maxLen])
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outLabel = np.zeros([len(domains), ])
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for i in range(len(domains)):
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domain = domains[i]
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curLabel = label[i]
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curFeature = np.zeros([maxLen, ])
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# print(domain + ' ' + str(len(domain)))
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for j in range(np.min([len(domain), maxLen])):
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# print(j)
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curCharacter = domain[-j]
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if curCharacter in characterDict:
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curFeature[j] = characterDict[curCharacter]
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outFeature[i] = curFeature
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outLabel[i] = curLabel
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return (outFeature, outLabel)
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def getFeatureVecForDomain(domain, characterDict, maxLen=40):
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curFeature = np.zeros([maxLen, ])
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for j in range(np.min([len(domain), maxLen])):
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# print(j)
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curCharacter = domain[-j]
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if curCharacter in characterDict:
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curFeature[j] = characterDict[curCharacter]
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return curFeature
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def getFlowFeatures(curDataLine):
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useKeys = ['duration', 'bytes_down', 'bytes_up']
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curFeature = np.zeros([len(useKeys), ])
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for i in range(len(useKeys)):
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curKey = useKeys[i]
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try:
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curFeature[i] = np.log1p(curDataLine[curKey], dtype='float32')
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except:
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pass
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return curFeature
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def getChunksFromUserDataFrame(dataFrame, windowSize=10, overlapping=False,
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maxLengthInSeconds=300):
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# print('maxLength: ' + str(maxLengthInSeconds))
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maxMilliSeconds = maxLengthInSeconds * 1000
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outDomainLists = []
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outDFFrames = []
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if overlapping == False:
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numBlocks = int(np.ceil(float(len(dataFrame)) / float(windowSize)))
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userIDs = np.arange(len(dataFrame))
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for blockID in np.arange(numBlocks):
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curIDs = userIDs[(blockID * windowSize):((blockID + 1) * windowSize)]
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# print(curIDs)
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useData = dataFrame.iloc[curIDs]
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curDomains = useData['domain']
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if maxLengthInSeconds != -1:
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curMinMilliSeconds = np.min(useData['timeStamp']) + maxMilliSeconds
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underTimeOutIDs = np.where(np.array(useData['timeStamp']) <= curMinMilliSeconds)
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if len(underTimeOutIDs) != len(curIDs):
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curIDs = curIDs[underTimeOutIDs]
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useData = dataFrame.iloc[curIDs]
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curDomains = useData['domain']
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outDomainLists.append(list(curDomains))
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outDFFrames.append(useData)
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else:
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numBlocks = len(dataFrame) + 1 - windowSize
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userIDs = np.arange(len(dataFrame))
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for blockID in np.arange(numBlocks):
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curIDs = userIDs[blockID:blockID + windowSize]
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# print(curIDs)
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useData = dataFrame.iloc[curIDs]
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curDomains = useData['domain']
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if maxLengthInSeconds != -1:
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curMinMilliSeconds = np.min(useData['timeStamp']) + maxMilliSeconds
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underTimeOutIDs = np.where(np.array(useData['timeStamp']) <= curMinMilliSeconds)
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if len(underTimeOutIDs) != len(curIDs):
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curIDs = curIDs[underTimeOutIDs]
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useData = dataFrame.iloc[curIDs]
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curDomains = useData['domain']
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outDomainLists.append(list(curDomains))
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outDFFrames.append(useData)
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return (outDomainLists, outDFFrames)
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def createTrainData(domainLists, dfLists, charachterDict, maxLen, threshold=3,
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flagUseCiscoFeatures=False, urlSIPDIct=dict,
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windowSize=10):
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if 'hits' in dfLists[0].keys():
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hitName = 'hits'
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elif 'virusTotalHits' in dfLists[0].keys():
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hitName = 'virusTotalHits'
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numFlowFeatures = 3
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numCiscoFeatures = 30
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numFeatures = numFlowFeatures
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if flagUseCiscoFeatures:
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numFeatures += numCiscoFeatures
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outputFeatures = []
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label = []
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hits = []
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trainNames = []
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for i in range(windowSize):
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outputFeatures.append(np.zeros([len(domainLists), maxLen]))
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outputFeatures.append(np.zeros([len(domainLists), numFeatures]))
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for i in tqdm(np.arange(len(domainLists)), miniters=10):
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curCounter = 0
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# print('len domainList: ' + str(len(domainLists[i])))
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# print('len df: ' + str(len(dfLists[i])))
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for j in range(np.min([windowSize, len(domainLists[i])])):
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outputFeatures[curCounter][i, :] = getFeatureVecForDomain(domainLists[i][j], charachterDict, maxLen)
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curCounter += 1
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if flagUseCiscoFeatures:
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outputFeatures[curCounter][i, 0:numFlowFeatures] = getFlowFeatures(dfLists[i].iloc[j])
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outputFeatures[curCounter][i, numFlowFeatures:] = getCiscoFeatures(dfLists[i].iloc[j], urlSIPDIct)
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else:
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outputFeatures[curCounter][i, :] = getFlowFeatures(dfLists[i].iloc[j])
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curCounter += 1
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curLabel = 0.0
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if np.max(dfLists[i][hitName]) >= threshold:
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curLabel = 1.0
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elif np.max(dfLists[i][hitName]) == -1:
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curLabel = -1.0
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elif np.max(dfLists[i][hitName]) > 0 and np.max(dfLists[i][hitName]) < threshold:
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curLabel = -2.0
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label.append(curLabel)
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hits.append(np.max(dfLists[i][hitName]))
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trainNames.append(np.unique(dfLists[i]['user_hash']))
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return (outputFeatures, np.array(label), np.array(hits), np.array(trainNames))
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def transformStringListToNumpyArray(listString):
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listString = listString.replace('[', '').replace(']', '')
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return np.array(listString.split(','), dtype='float32')
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def getCiscoFeatureDict(csvPathList):
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outDict = dict()
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for path in tqdm(csvPathList, miniters=1):
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fobj = open(path, 'r')
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csvReader = csv.DictReader(fobj, delimiter=',')
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for row in csvReader:
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urlSIPString = row['Domain'] + row['ServerIP']
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ciscoFeatures = row['CiscoFeature']
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outDict[urlSIPString] = transformStringListToNumpyArray(ciscoFeatures)
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# if len(outDict) % 10000 == 0:
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# print('numbers in dict: ' + str(len(outDict)))
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return outDict
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if __name__ == "__main__":
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pass
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